Kall and Wallace, Wiley, Stochastic Programming, 1994, here.
‘Stochastic Programming’ is the first textbook to provide a thorough and self-contained introduction to the subject. Carefully written to cover all necessary background material from both linear and non-linear programming, as well as probability theory, the book draws together the methods and techniques previously described in disparate sources. After introducing the terms and modelling issues when randomness is introduced in a deterministic mathematical programming model, the authors cover decision trees and dynamic programming, recourse problems, probabilistic constraints, preprocessing and network problems. Exercises are provided at the end of each chapter. Throughout, the emphasis is on the appropriate use of the techniques, rather than on the underlying mathematical proofs and theories, making the book ideal for researchers and students in mathematical programming and operations research who wish to develop their skills in stochastic programming.
Romisch, Stochastic Programming: Tutorial Part 1, 2012, here.
Greenberg and Morrison, Robust Optimization, here.
MPS format, here.
GLPK, here. they have an open source AMPL called GMPL.
The GLPK (GNU Linear Programming Kit) package is intended for solving large-scale linear programming (LP), mixed integer programming (MIP), and other related problems. It is a set of routines written in ANSI C and organized in the form of a callable library.
GLPK supports the GNU MathProg modeling language, which is a subset of the AMPL language.
The GLPK package includes the following main components:
primal and dual simplex methods
primal-dual interior-point method
translator for GNU MathProg
application program interface (API)
stand-alone LP/MIP solver
The NEOS Server is a free internet-based service for solving numerical optimization problems. Hosted by the Wisconsin Institutes for Discovery at the University of Wisconsin in Madison, the NEOS Server provides access to more than 60 state-of-the-art solvers in more than a dozen optimization categories. The NEOS Server offers a variety of interfaces for accessing the solvers. Solvers hosted by the University of Wisconsin in Madison run on distributed high-performance machines enabled by the HTCondor software; remote solvers run on machines at Argonne National Laboratory, Arizona State University, the University of Klagenfurt in Austria, and the University of Minho in Portugal. The NEOS Guide web site complements the NEOS Server, showcasing optimization case studies, presenting optimization information and resources, and providing background information on the NEOS Server.
Read the feature article about NEOS, Optimizing the World, One Problem at a Time, at the Wisconsin Institute for Discovery web site.
The AMPL system supports the entire optimization modeling lifecycle — formualation, testing, deployment, and maintenance — in an integrated way promotes rapid development and reliable results. Using a high-level algebraic representation that describes optimization models in the same ways that people think about them, AMPL can provide the head start you need to successfully implement large-scale optimization projects.
AMPL integrates a modeling language for describing optimization data, variables, objectives, and constraints; a command language for debugging models and analyzing results; and a scripting language for manipulating data and implementing optimization strategies. All use the same concepts to promote streamlined application-building.
New AMPL APIs offer programming interfaces for embedding AMPL models into enterprise systems written in a variety of languages.